Using emerging on-farm technologies large data sets will be generated and combined with novel data management techniques to develop/test/validate tools that better predict corn N requirement.
Using emerging on-farm technologies large data sets will be generated and combined with novel data management techniques to develop/test/validate tools that better predict corn N requirement.
Low fertilizer nitrogen (N) use efficiency in corn (less than 50 percent) is an economic inefficiency with environmental consequences. Optimal fertilizer N rate in corn varies from year to year and in different areas, making it difficult to predict. Large temporal variation is demonstrated using data from an ongoing, long term trial. Low rates result in economic loss, while rates that are too high result in both economic losses and negative environmental impact. Existing corn N decision support tools have had little success in predicting spatial and temporal variation in N requirement, in large part because they do not consider moisture interactions, as variation in moisture is associated with optimal N rates.
Historically, data required to develop/test/validate improved corn decision support tools has been generated by researchers associated with academic institutions or government. Due to limited resources, development of the substantial data required is slow when this approach is used. Technologies are emerging that will enable farmers to participate in rapid generation of large amounts of nitrogen response data.
The objective of this proposal is to develop, in collaboration with stakeholders, a practical protocol whereby farmers actively participate in generation of a corn nitrogen response database for development/testing/validation of a corn N decision support tool that considers the effects of moisture on both soil N supply and corn N demand.
Improved prediction of optimal nitrogen rate in corn will have direct economic and environmental benefits. The value of a corn N decision support tool that accounts for N x soil water interactions is expected to increase over time since frequency of water deficits will increase for three reasons; 1) crop transpiration is expected to increase driven in part by higher yields, but also by higher expected temperatures and vapor pressure deficits during the growing season, 2) climate change will alter precipitation patterns and evapotranspiration, and 3) trends toward simpler rotations will reduce soil water holding capacity and hence drought resilience. Additionally, the farmer participatory approach to data generation to be developed in this study can be used as a model system to generate data to address other agronomic problems.
Partners: OMAFRA, Grain Farmers of Ontario, NSERC, Monsanto Climate Fieldview, International Plant Nutrition Institute.
Collaborators and students: Dr. Graham Taylor, Ben Rosser, OMAFRA, Nicole Rabe, OMAFRA, Ian McDonald, OMAFRA, Joshua Nasielski (PhD), Caleb Niemeyer (MSc), Ken Janovicek (Research Ass.), Ramesh Eerpina (Research Tech.).
